Surface normals for pixel-aligned object

ABSTRACT

Methods and systems are disclosed for performing operations for applying augmented reality elements to a person depicted in an image. The operations include receiving an image that includes data representing a depiction of a person; generating a segmentation of the data representing the person depicted in the image; extracting a portion of the image corresponding to the segmentation of the data representing the person depicted in the image; applying a machine learning model to the portion of the image to predict a surface normal tensor for the data representing the depiction of the person, the surface normal tensor representing surface normals of each pixel within the portion of the image; and applying one or more augmented reality (AR) elements to the image based on the surface normal tensor.

CLAIM OF PRIORITY

This application claims the benefit of priority to Greece PatentApplication Serial No. 20220100284, filed Mar. 30, 2022, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to providing augmented realityexperiences using a messaging application.

BACKGROUND

Augmented Reality (AR) is a modification of a virtual environment. Forexample, in Virtual Reality (VR), a user is completely immersed in avirtual world, whereas in AR, the user is immersed in a world wherevirtual objects are combined or superimposed on the real world. An ARsystem aims to generate and present virtual objects that interactrealistically with a real-world environment and with each other.Examples of AR applications can include single or multiple player videogames, instant messaging systems, and the like.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some nonlimiting examples areillustrated in the figures of the accompanying drawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some examples.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a block diagram showing an example surface normal tensorcontrol system, according to some examples.

FIGS. 6, 7, and 8 are diagrammatic representations of outputs of thesurface normal tensor control system, in accordance with some examples.

FIG. 9 is a flowchart illustrating example operations of the surfacenormal tensor control system, according to some examples.

FIG. 10 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 11 is a block diagram showing a software architecture within whichexamples may be implemented.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examples.It will be evident, however, to those skilled in the art, that examplesmay be practiced without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

Typically, VR and AR systems display images representing a givenreal-world object, such as a user, by capturing an image of the objectand, in addition, obtaining a depth map using a depth sensor of thereal-world object depicted in the image. By processing the depth map andthe image together, the VR and AR systems can detect positioning of aobject in the image and can appropriately modify the object orbackground in the images. While such systems work well, the need for adepth sensor limits the scope of their applications. This is becauseadding depth sensors to user devices for the purpose of modifying imagesincreases the overall cost and complexity of the devices, making themless attractive.

Certain systems do away with the need to use depth sensors to modifyimages. For example, certain systems allow users to replace a backgroundin a videoconference in which a face of the user is detected.Specifically, such systems can use specialized techniques that areoptimized for recognizing a portion of the object, such as the face of auser, to identify the background in the images that depict the portionof the object (e.g., the user's face). These systems can then replaceonly those pixels that depict the background so that the real-worldbackground is replaced with an alternate background in the images. Suchsystems though are generally incapable of recognizing the entirety ofthe object, such as a whole body of a user. As such, if the object ismore than a threshold distance from the camera such that more than justthe portion object (e.g., the face of the user) is captured by thecamera, the replacement of the background with an alternate backgroundbegins to fail. In such cases, the image quality is severely impacted,and portions of the object (e.g., face and body of the user) can beinadvertently removed by the system as the system falsely identifiessuch portions as belonging to the background rather than the foregroundof the images.

These types of systems also fail to properly replace the background whenmore than one object is depicted in the image or video feed. Becausesuch systems are generally incapable of distinguishing the entirety ofthe object from a background, these systems are also unable to applyvisual effects to certain portions of the object. For example, systemsthat identify the face of a user may fail to apply visual effects toother portions of the user, such as the user's body, articles ofclothing, and the like.

The disclosed techniques improve the efficiency of using the electronicdevice by cropping out a portion of an image or video depicting a anobject (e.g., body of a person) in the image or video and applying amachine learning model to the cropped out object to estimate both asegmentation of the object and a surface normal tensor. The surfacenormal tensor can represent the surface normal of each pixel that ispart of the segmentation of the object depicted in the image. Thisenables the disclosed systems to apply one or more AR effects only tothe object (e.g., body of a person) depicted in the image withoutaffecting the background. As a result, a more realistic display of theAR effects can be provided to the user which significantly improves theillusion that such AR effects are part of the real-world environment.

In some examples, the surface normals of the pixels of the object arecomputed or provided relative to a camera or surface normal of thecamera used to capture the image or video. In some examples, thedisclosed techniques can change lighting effects and reflections on theobject, such as by adding effects to a human body and/or fashion itemsor garments worn by the human body. The AR effects can be applied basedon a geometry of the object, such as a body of the person, hair of theperson, clothing of the person, and/or one or more accessories worn bythe person. In some examples, artificial light can be applied to datarepresenting the object depicted in the image based on the surfacenormal tensor. In such cases, the surface normal tensor providesinformation about details of the object, such as wrinkles of skin orfashion items worn by the person to modify the shadows and/orreflections of the artificial light in a realistic manner. For example,a portion of the one or more AR elements that overlays the one or morewrinkles can be bent based on the surface normal tensor. In someexamples, two AR elements, such as 3D columns, can be generated toextend from respective pixels along respective directions of surfacenormals of such pixels.

This improves the overall experience of the user in using the electronicdevice. Also, by performing such segmentations without using a depthsensor, the overall amount of system resources needed to accomplish atask is reduced.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104 and other external applications 109 (e.g., third-partyapplications). Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 (e.g., hosted on respectiveother client devices 102), a messaging server system 108 and externalapp(s) servers 110 via a network 112 (e.g., the Internet). A messagingclient 104 can also communicate with locally-hosted third-partyapplications, such as external apps 109 using Application ProgrammingInterfaces (APIs).

The client device 102 may operate as a standalone device or may becoupled (e.g., networked) to other machines. In a networked deployment,the client device 102 may operate in the capacity of a server machine ora client machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Theclient device 102 may comprise, but not be limited to, a servercomputer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a set-top box (STB), a personaldigital assistant (PDA), an entertainment media system, a cellulartelephone, a smartphone, a mobile device, a wearable device (e.g., asmartwatch), a smart home device (e.g., a smart appliance), other smartdevices, a web appliance, a network router, a network switch, a networkbridge, or any machine capable of executing the disclosed operations.Further, while only a single client device 102 is illustrated, the term“client device” shall also be taken to include a collection of machinesthat individually or jointly execute the disclosed operations.

In some examples, the client device 102 can include AR glasses or an ARheadset in which virtual content or AR/VR element(s) is/are displayedwithin lenses of the glasses while a user views a real-world environmentthrough the lenses. For example, an image can be presented on atransparent display that allows a user to simultaneously view virtualcontent presented on the display and real-world objects.

A messaging client 104 is able to communicate and exchange data withother messaging clients 104 and with the messaging server system 108 viathe network 112. The data exchanged between messaging clients 104, andbetween a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 112 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces of the messaging client 104.

Turning now specifically to the messaging server system 108, an APIserver 116 is coupled to, and provides a programmatic interface to,application servers 114. The application servers 114 are communicativelycoupled to a database server 120, which facilitates access to a database126 that stores data associated with messages processed by theapplication servers 114. Similarly, a web server 128 is coupled to theapplication servers 114 and provides web-based interfaces to theapplication servers 114. To this end, the web server 128 processesincoming network requests over the Hypertext Transfer Protocol (HTTP)and several other related protocols.

The API server 116 receives and transmits message data (e.g., commandsand message payloads) between the client device 102 and the applicationservers 114. Specifically, the API server 116 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 114. The API server 116 exposes various functionssupported by the application servers 114, including accountregistration; login functionality; the sending of messages, via theapplication servers 114, from a particular messaging client 104 toanother messaging client 104; the sending of media files (e.g., imagesor video) from a messaging client 104 to a messaging server 118, and forpossible access by another messaging client 104; the settings of acollection of media data (e.g., story); the retrieval of a list offriends of a user of a client device 102; the retrieval of suchcollections; the retrieval of messages and content; the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph); the location of friends within a social graph; and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 114 host a number of server applications andsubsystems, including, for example, a messaging server 118, an imageprocessing server 122, and a social network server 124. The messagingserver 118 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor- and memory-intensive processingof data may also be performed server-side by the messaging server 118,in view of the hardware requirements for such processing.

The application servers 114 also include an image processing server 122that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 118.

Image processing server 122 is used to implement scan functionality ofthe augmentation system 208 (shown in FIG. 2 ). Scan functionalityincludes activating and providing one or more AR experiences on a clientdevice 102 when an image is captured by the client device 102.Specifically, the messaging client 104 on the client device 102 can beused to activate a camera. The camera displays one or more real-timeimages or a video to a user along with one or more icons or identifiersof one or more AR experiences. The user can select a given one of theidentifiers to launch the corresponding AR experience or perform adesired image modification (e.g., replacing a garment being worn by auser in a video or recoloring the garment worn by the user in the videoor modifying the garment based on a gesture performed by the user).

The social network server 124 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 118. To this end, the social network server 124maintains and accesses an entity graph 308 (as shown in FIG. 3 ) withinthe database 126. Examples of functions and services supported by thesocial network server 124 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

Returning to the messaging client 104, features and functions of anexternal resource (e.g., a third-party application 109 or applet) aremade available to a user via an interface of the messaging client 104.The messaging client 104 receives a user selection of an option tolaunch or access features of an external resource (e.g., a third-partyresource), such as external apps 109. The external resource may be athird-party application (external apps 109) installed on the clientdevice 102 (e.g., a “native app”), or a small-scale version of thethird-party application (e.g., an “applet”) that is hosted on the clientdevice 102 or remote of the client device 102 (e.g., on third-partyservers 110). The small-scale version of the third-party applicationincludes a subset of features and functions of the third-partyapplication (e.g., the full-scale, native version of the third-partystandalone application) and is implemented using a markup-languagedocument. In one example, the small-scale version of the third-partyapplication (e.g., an “applet”) is a web-based, markup-language versionof the third-party application and is embedded in the messaging client104. In addition to using markup-language documents (e.g., a .*ml file),an applet may incorporate a scripting language (e.g., a .*j s file or aj son file) and a style sheet (e.g., a .*ss file).

In response to receiving a user selection of the option to launch oraccess features of the external resource (external app 109), themessaging client 104 determines whether the selected external resourceis a web-based external resource or a locally-installed externalapplication. In some cases, external applications 109 that are locallyinstalled on the client device 102 can be launched independently of andseparately from the messaging client 104, such as by selecting an icon,corresponding to the external application 109, on a home screen of theclient device 102. Small-scale versions of such external applicationscan be launched or accessed via the messaging client 104 and, in someexamples, no or limited portions of the small-scale external applicationcan be accessed outside of the messaging client 104. The small-scaleexternal application can be launched by the messaging client 104receiving, from an external app(s) server 110, a markup-languagedocument associated with the small-scale external application andprocessing such a document.

In response to determining that the external resource is alocally-installed external application 109, the messaging client 104instructs the client device 102 to launch the external application 109by executing locally-stored code corresponding to the externalapplication 109. In response to determining that the external resourceis a web-based resource, the messaging client 104 communicates with theexternal app(s) servers 110 to obtain a markup-language documentcorresponding to the selected resource. The messaging client 104 thenprocesses the obtained markup-language document to present the web-basedexternal resource within a user interface of the messaging client 104.

The messaging client 104 can notify a user of the client device 102, orother users related to such a user (e.g., “friends”), of activity takingplace in one or more external resources. For example, the messagingclient 104 can provide participants in a conversation (e.g., a chatsession) in the messaging client 104 with notifications relating to thecurrent or recent use of an external resource by one or more members ofa group of users. One or more users can be invited to join in an activeexternal resource or to launch a recently-used but currently inactive(in the group of friends) external resource. The external resource canprovide participants in a conversation, each using a respectivemessaging client 104, with the ability to share an item, status, state,or location in an external resource with one or more members of a groupof users into a chat session. The shared item may be an interactive chatcard with which members of the chat can interact, for example, to launchthe corresponding external resource, view specific information withinthe external resource, or take the member of the chat to a specificlocation or state within the external resource. Within a given externalresource, response messages can be sent to users on the messaging client104. The external resource can selectively include different media itemsin the responses, based on a current context of the external resource.

The messaging client 104 can present a list of the available externalresources (e.g., third-party or external applications 109 or applets) toa user to launch or access a given external resource. This list can bepresented in a context-sensitive menu. For example, the iconsrepresenting different ones of the external application 109 (or applets)can vary based on how the menu is launched by the user (e.g., from aconversation interface or from a non-conversation interface).

The messaging client 104 can present to a user one or more ARexperiences that can be controlled and presented on a body of a person(or user) and/or an article of clothing, such as a shirt (fashion itemor upper garment), worn by the person (or user) depicted in the image.As an example, the messaging client 104 can detect a person in an imageor video captured by the client device 102. The messaging client 104 cancrop a portion of the image depicting the person. The messaging client104 can apply a machine learning model to the cropped portion togenerate a segmentation of the body of the person depicted in the imageand to generate a surface normal tensor for the body of the person.Using the surface normal tensor and the segmentation of the body, themessaging client 104 can apply one or more AR elements to the bodyand/or the article of clothing (or fashion item), such as a shirt,article of clothing, upper garment, fashion item, dress, pants, shorts,skirts, jackets, t-shirts, blouses, glasses, jewelry, a hat, ear muffs,and so forth, in the image or video.

For example, the surface normal tensor indicates an estimated angle ofeach pixel in the portion of the image corresponding to the bodysegmentation, such as relative to a camera used to capture the image(e.g., relative to a surface normal of the camera used to capture theimage or video). This enables the messaging client 104 to present one ormore AR elements on the body and/or clothing depicted in the image basedon the surface normal tensor.

In some examples, the messaging client 104 can determine that light isbeing focused on a portion of the body of the depicted person from afirst direction based on the surface normal tensor. In response, themessaging client 104 can modify pixel values of the portion of the bodyof the depicted person to re-focus the light on the body of the personfrom a second direction based on the surface normal tensor (e.g., thesurface normals of each pixel that is in the portion of the body).Specifically, the messaging client 104 can determine that a first pixelin the portion of the image is pointing towards a given directionrelative to the camera or surface normal of the camera. In such cases,the messaging client 104 can modify the first pixel to render areflection of the re-focused light from the second direction towards thegiven direction.

For example, the light in the image depicting the person can be focusedfrom a top of the image towards a bottom of the image. Namely, aspotlight can be presented above the person depicted in the image andcan point downwards towards the floor depicted in the image. In suchcases, the messaging client 104 can modify the pixel values to re-focusartificial light from the bottom towards the top. For example, themessaging client 104 can remove the spotlight or light coming from thetop of the image depicting the person and can render a display of ARlight originating from a floor depicted in the image. The messagingclient 104 can control reflections off of a person depicted in the imagebased on the surface normal tensor of the body of the person.Specifically, the messaging client 104 can determine how light that isdirected towards each given pixel of the person is reflected or absorbedbased on the corresponding angle of each pixel relative to the cameraand based on the angle of each pixel relative to the point of origin ofthe AR light. This creates a realistic illusion that light isoriginating from a bottom of the image as the manner of reflection andabsorption of the light on the person depicted in the image ispreserved.

In some examples, the messaging client 104 can generate a 3D graphic asthe AR elements. The 3D graphic can be associated with a group of pixelshaving a certain quantity of pixels. For example, a group of pixels thatare within a specified region (e.g., a square region or circular regionhaving a certain size) can be associated with each 3D graphic. In suchcases, the messaging client 104 can identify a first group of pixelsthat are within a first portion of the segmentation of the body of theperson depicted in the image. The messaging client 104 can obtain thesurface normal tensor for that first group of pixels and can generate anaverage surface normal that represents the average of the surfacenormals of the first group of pixels. The messaging client 104 can thenretrieve the 3D graphic and align the 3D graphic on top of the firstgroup of pixels and angle the 3D graphic along the average of thesurface normals. This makes the 3D graphic appear to be extending fromthe first group of pixels corresponding to the first portion of thesegmentation of the body of the person. The messaging client 104 canperform a similar operation for a second group of pixels that are withina second portion of the segmentation to generate a second 3D graphic fordisplay on top of the second group of pixels. This process can berepeated until all of the pixel groups within the segmentation of thebody are associated with respective 3D graphics.

The messaging client 104 continuously or periodically recomputes andre-estimates the surface normal tensor as new images or videos arereceived. Specifically, the messaging client 104 can track movement ofthe person depicted in the image or video across frames of the image orvideo. As the person moves, the messaging client 104 can recompute andre-estimate the surface normal tensor in the portion of the imagecorresponding to the depicted person. The messaging client 104 cancontinuously or periodically modify the AR elements presented on theperson and specifically modify the way in which light is reflected orabsorbed by the pixels corresponding to the person and the way in whichshadows are generated based on changes to the surface normal tensor.

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 114. The messaging system 100 embodies a numberof subsystems, which are supported on the client side by the messagingclient 104 and on the sever side by the application servers 114. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, an augmentation system 208, a mapsystem 210, a game system 212, and an external resource system 220.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 118. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 further includes a curationinterface 206 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface206 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 208 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system208 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 208 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 208 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text, a graphical element, or image that canbe overlaid on top of a photograph taken by the client device 102. Inanother example, the media overlay includes an identification of alocation overlay (e.g., Venice beach), a name of a live event, or a nameof a merchant overlay (e.g., Beach Coffee House). In another example,the augmentation system 208 uses the geolocation of the client device102 to identify a media overlay that includes the name of a merchant atthe geolocation of the client device 102. The media overlay may includeother indicia associated with the merchant. The media overlays may bestored in the database 126 and accessed through the database server 120.

In some examples, the augmentation system 208 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 208 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 208 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 208 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time. The augmentation system 208 communicates withthe image processing server 122 to obtain AR experiences and presentsidentifiers of such experiences in one or more user interfaces (e.g., asicons over a real-time image or video or as thumbnails or icons ininterfaces dedicated for presented identifiers of AR experiences). Oncean AR experience is selected, one or more images, videos, or ARgraphical elements are retrieved and presented as an overlay on top ofthe images or video captured by the client device 102. In some cases,the camera is switched to a front-facing view (e.g., the front-facingcamera of the client device 102 is activated in response to activationof a particular AR experience) and the images from the front-facingcamera of the client device 102 start being displayed on the clientdevice 102 instead of the rear-facing camera of the client device 102.The one or more images, videos, or AR graphical elements are retrievedand presented as an overlay on top of the images that are captured anddisplayed by the front-facing camera of the client device 102.

In other examples, the augmentation system 208 is able to communicateand exchange data with another augmentation system 208 on another clientdevice 102 and with the server via the network 112. The data exchangedcan include a session identifier that identifies the shared AR session,a transformation between a first client device 102 and a second clientdevice 102 (e.g., a plurality of client devices 102 include the firstand second devices) that is used to align the shared AR session to acommon point of origin, a common coordinate frame, functions (e.g.,commands to invoke functions), and other payload data (e.g., text,audio, video, or other multimedia data).

The augmentation system 208 sends the transformation to the secondclient device 102 so that the second client device 102 can adjust the ARcoordinate system based on the transformation. In this way, the firstand second client devices 102 synch up their coordinate systems andframes for displaying content in the AR session. Specifically, theaugmentation system 208 computes the point of origin of the secondclient device 102 in the coordinate system of the first client device102. The augmentation system 208 can then determine an offset in thecoordinate system of the second client device 102 based on the positionof the point of origin from the perspective of the second client device102 in the coordinate system of the second client device 102. Thisoffset is used to generate the transformation so that the second clientdevice 102 generates AR content according to a common coordinate systemor frame as the first client device 102.

The augmentation system 208 can communicate with the client device 102to establish individual or shared AR sessions. The augmentation system208 can also be coupled to the messaging server 118 to establish anelectronic group communication session (e.g., group chat, instantmessaging) for the client devices 102 in a shared AR session. Theelectronic group communication session can be associated with a sessionidentifier provided by the client devices 102 to gain access to theelectronic group communication session and to the shared AR session. Inone example, the client devices 102 first gain access to the electronicgroup communication session and then obtain the session identifier inthe electronic group communication session that allows the clientdevices 102 to access the shared AR session. In some examples, theclient devices 102 are able to access the shared AR session without aidor communication with the augmentation system 208 in the applicationservers 114.

The map system 210 provides various geographic location functions andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 210 enables thedisplay of user icons or avatars (e.g., stored in profile data 316) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 212 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games (e.g., web-based games orweb-based applications) that can be launched by a user within thecontext of the messaging client 104 and played with other users of themessaging system 100. The messaging system 100 further enables aparticular user to invite other users to participate in the play of aspecific game by issuing invitations to such other users from themessaging client 104. The messaging client 104 also supports both voiceand text messaging (e.g., chats) within the context of gameplay,provides a leaderboard for the games, and also supports the provision ofin-game rewards (e.g., coins and items).

The external resource system 220 provides an interface for the messagingclient 104 to communicate with external app(s) servers 110 to launch oraccess external resources. Each external resource (apps) server 110hosts, for example, a markup language (e.g., HTML5) based application orsmall-scale version of an external application (e.g., game, utility,payment, or ride-sharing application that is external to the messagingclient 104). The messaging client 104 may launch a web-based resource(e.g., application) by accessing the HTML5 file from the externalresource (apps) servers 110 associated with the web-based resource. Incertain examples, applications hosted by external resource servers 110are programmed in JavaScript leveraging a Software Development Kit (SDK)provided by the messaging server 118. The SDK includes APIs withfunctions that can be called or invoked by the web-based application. Incertain examples, the messaging server 118 includes a JavaScript librarythat provides a given third-party resource access to certain user dataof the messaging client 104. HTML5 is used as an example technology forprogramming games, but applications and resources programmed based onother technologies can be used.

In order to integrate the functions of the SDK into the web-basedresource, the SDK is downloaded by an external resource (apps) server110 from the messaging server 118 or is otherwise received by theexternal resource (apps) server 110. Once downloaded or received, theSDK is included as part of the application code of a web-based externalresource. The code of the web-based resource can then call or invokecertain functions of the SDK to integrate features of the messagingclient 104 into the web-based resource.

The SDK stored on the messaging server 118 effectively provides thebridge between an external resource (e.g., third-party or externalapplications 109 or applets and the messaging client 104). This providesthe user with a seamless experience of communicating with other users onthe messaging client 104, while also preserving the look and feel of themessaging client 104. To bridge communications between an externalresource and a messaging client 104, in certain examples, the SDKfacilitates communication between external resource servers 110 and themessaging client 104. In certain examples, a WebViewJavaScriptBridgerunning on a client device 102 establishes two one-way communicationchannels between an external resource and the messaging client 104.Messages are sent between the external resource and the messaging client104 via these communication channels asynchronously. Each SDK functioninvocation is sent as a message and callback. Each SDK function isimplemented by constructing a unique callback identifier and sending amessage with that callback identifier.

By using the SDK, not all information from the messaging client 104 isshared with external resource servers 110. The SDK limits whichinformation is shared based on the needs of the external resource. Incertain examples, each external resource server 110 provides an HTML5file corresponding to the web-based external resource to the messagingserver 118. The messaging server 118 can add a visual representation(such as a box art or other graphic) of the web-based external resourcein the messaging client 104. Once the user selects the visualrepresentation or instructs the messaging client 104 through a graphicaluser interface of the messaging client 104 to access features of theweb-based external resource, the messaging client 104 obtains the HTML5file and instantiates the resources necessary to access the features ofthe web-based external resource.

The messaging client 104 presents a graphical user interface (e.g., alanding page or title screen) for an external resource. During, before,or after presenting the landing page or title screen, the messagingclient 104 determines whether the launched external resource has beenpreviously authorized to access user data of the messaging client 104.In response to determining that the launched external resource has beenpreviously authorized to access user data of the messaging client 104,the messaging client 104 presents another graphical user interface ofthe external resource that includes functions and features of theexternal resource. In response to determining that the launched externalresource has not been previously authorized to access user data of themessaging client 104, after a threshold period of time (e.g., 3 seconds)of displaying the landing page or title screen of the external resource,the messaging client 104 slides up (e.g., animates a menu as surfacingfrom a bottom of the screen to a middle of or other portion of thescreen) a menu for authorizing the external resource to access the userdata. The menu identifies the type of user data that the externalresource will be authorized to use. In response to receiving a userselection of an accept option, the messaging client 104 adds theexternal resource to a list of authorized external resources and allowsthe external resource to access user data from the messaging client 104.In some examples, the external resource is authorized by the messagingclient 104 to access the user data in accordance with an OAuth 2framework.

The messaging client 104 controls the type of user data that is sharedwith external resources based on the type of external resource beingauthorized. For example, external resources that include full-scaleexternal applications (e.g., a third-party or external application 109)are provided with access to a first type of user data (e.g., onlytwo-dimensional (2D) avatars of users with or without different avatarcharacteristics). As another example, external resources that includesmall-scale versions of external applications (e.g., web-based versionsof third-party applications) are provided with access to a second typeof user data (e.g., payment information, 2D avatars of users,three-dimensional (3D) avatars of users, and avatars with various avatarcharacteristics). Avatar characteristics include different ways tocustomize a look and feel of an avatar, such as different poses, facialfeatures, clothing, and so forth.

A surface normal tensor control system 224 crops out portion of an imageor video depicting a body of a person depicted in the image or video andapplies a machine learning model (e.g., convolutional neural network orother artificial neural network) to the cropped out body to estimateboth a segmentation of the body and a surface normal tensor. The surfacenormal tensor can represent the surface normal of each pixel that ispart of the segmentation of the body depicted in the image. This enablesthe surface normal tensor control system 224 to apply one or more AReffects only to the body of the person depicted in the image withoutaffecting the background. As a result, a more realistic display of theAR effects can be provided to the user which significantly improves theillusion that such AR effects are part of the real-world environment. Anillustrative implementation of the surface normal tensor control system224 is shown and described in connection with FIG. 5 below.

Specifically, the surface normal tensor control system 224 is acomponent that can be accessed by an AR/VR application implemented onthe client device 102. The AR/VR application uses an RGB camera tocapture a monocular image of a user or person and the garment orgarments (alternatively referred to as fashion item(s)) worn by the useror person. The AR/VR application applies various trained machinelearning techniques on the captured image of the person to generate orestimate the surface normal tensor for the pixels of the person and toapply one or more AR visual effects to the portions of the image thatdepict the person without modifying other portions of the image that donot depict the person (e.g., background portions). The body segmentationis used to distinguish the person depicted in the image from otherobjects or elements depicted in the image. In some implementations, theAR/VR application continuously captures images of the person in realtime or periodically to continuously or periodically update the appliedone or more visual effects. This allows the user to move around in thereal world and see the one or more visual effects update in real time.

In order for the AR/VR application to apply the one or more visualeffects directly from a captured RGB image, the AR/VR applicationobtains a trained machine learning technique from the surface normaltensor control system 224. The trained machine learning techniqueprocesses the captured RGB image to generate a segmentation and surfacenormal tensor from the captured image that corresponds to the persondepicted in the captured RGB image.

In training, the surface normal tensor control system 224 obtains afirst plurality of input training images that include a training portionrepresenting a person depicted in an image and a correspondingground-truth surface normal tensor. A machine learning technique (ormachine learning model) (e.g., a deep neural network) is trained basedon features of the plurality of training images. Specifically, themachine learning technique extracts one or more features from a giventraining image and estimates (predicts) a segmentation and surfacenormal tensor for the body depicted in the given training image. Themachine learning technique obtains the ground truth informationcorresponding to the training image and adjusts or updates one or morecoefficients or parameters to improve subsequent estimations ofsegmentations and surface normal tensors of the person depicted in theimage.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 126 of the messaging server system 108,according to certain examples. While the content of the database 126 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 126 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage, and included within the message data stored in the messagetable 302, are described below with reference to FIG. 4 .

An entity table 306 stores entity data, and is linked (e.g.,referentially) to an entity graph 308 and profile data 316. Entities forwhich records are maintained within the entity table 306 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 308 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization),interested-based, or activity-based, merely for example.

The profile data 316 stores multiple types of profile data about aparticular entity. The profile data 316 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 316 includes, for example, a user name,telephone number, address, and settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100 and onmap interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 316 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 126 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 304) and images (for which data is stored in an image table312).

The database 126 can also store data pertaining to individual and sharedAR sessions. This data can include data communicated between an ARsession client controller of a first client device 102 and another ARsession client controller of a second client device 102, and datacommunicated between the AR session client controller and theaugmentation system 208. Data can include data used to establish thecommon coordinate frame of the shared AR scene, the transformationbetween the devices, the session identifier, images depicting a body,skeletal joint positions, wrist joint positions, feet, and so forth.

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 312includes AR content items (e.g., corresponding to applying ARexperiences). An AR content item or AR item may be a real-time specialeffect and sound that may be added to an image or a video.

As described above, augmentation data includes AR content items,overlays, image transformations, AR images, AR logos or emblems, andsimilar terms that refer to modifications that may be applied to imagedata (e.g., videos or images). This includes real-time modifications,which modify an image as it is captured using device sensors (e.g., oneor multiple cameras) of a client device 102 and then displayed on ascreen of the client device 102 with the modifications. This alsoincludes modifications to stored content, such as video clips in agallery that may be modified. For example, in a client device 102 withaccess to multiple AR content items, a user can use a single video clipwith multiple AR content items to see how the different AR content itemswill modify the stored clip. For example, multiple AR content items thatapply different pseudorandom movement models can be applied to the samecontent by selecting different AR content items for the content.Similarly, real-time video capture may be used with an illustratedmodification to show how video images currently being captured bysensors of a client device 102 would modify the captured data. Such datamay simply be displayed on the screen and not stored in memory, or thecontent captured by the device sensors may be recorded and stored inmemory with or without the modifications (or both). In some systems, apreview feature can show how different AR content items will look withindifferent windows in a display at the same time. This can, for example,enable multiple windows with different pseudorandom animations to beviewed on a display at the same time.

Data and various systems using AR content items or other such transformsystems to modify content using this data can thus involve detection ofobjects (e.g., faces, hands, bodies, cats, dogs, surfaces, objects,etc.), tracking of such objects as they leave, enter, and move aroundthe field of view in video frames, and the modification ortransformation of such objects as they are tracked. In various examples,different methods for achieving such transformations may be used. Someexamples may involve generating a 3D mesh model of the object or objectsand using transformations and animated textures of the model within thevideo to achieve the transformation. In other examples, tracking ofpoints on an object may be used to place an image or texture (which maybe 2D or 3D) at the tracked position. In still further examples, neuralnetwork analysis of video frames may be used to place images, models, ortextures in content (e.g., images or frames of video). AR content itemsthus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of an object's elements, characteristic points for each element ofan object are calculated (e.g., using an Active Shape Model (ASM) orother known methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshis used in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh and then variousareas based on the points are generated. The elements of the object arethen tracked by aligning the area for each element with a position foreach of the at least one elements, and properties of the areas can bemodified based on the request for modification, thus transforming theframes of the video stream. Depending on the specific request formodification, properties of the mentioned areas can be transformed indifferent ways. Such modifications may involve changing color of areas;removing at least some part of areas from the frames of the videostream; including one or more new objects into areas which are based ona request for modification; and modifying or distorting the elements ofan area or object. In various examples, any combination of suchmodifications or other similar modifications may be used. For certainmodels to be animated, some characteristic points can be selected ascontrol points to be used in determining the entire state-space ofoptions for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ASMalgorithm is applied to the face region of an image to detect facialfeature reference points.

Other methods and algorithms suitable for face detection can be used.For example, in some examples, features are located using a landmark,which represents a distinguishable point present in most of the imagesunder consideration. For facial landmarks, for example, the location ofthe left eye pupil may be used. If an initial landmark is notidentifiable (e.g., if a person has an eyepatch), secondary landmarksmay be used. Such landmark identification procedures may be used for anysuch objects. In some examples, a set of landmarks forms a shape. Shapescan be represented as vectors using the coordinates of the points in theshape. One shape is aligned to another with a similarity transform(allowing translation, scaling, and rotation) that minimizes the averageEuclidean distance between shape points. The mean shape is the mean ofthe aligned training shapes.

In some examples, a search is started for landmarks from the mean shapealigned to the position and size of the face determined by a global facedetector. Such a search then repeats the steps of suggesting a tentativeshape by adjusting the locations of shape points by template matching ofthe image texture around each point and then conforming the tentativeshape to a global shape model until convergence occurs. In some systems,individual template matches are unreliable, and the shape model poolsthe results of the weak template matches to form a stronger overallclassifier. The entire search is repeated at each level in an imagepyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client 104 operating on theclient device 102. The transformation system operating within themessaging client 104 determines the presence of a face within the imageor video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransformation system initiates a process to convert the image of theuser to reflect the selected modification icon (e.g., generate a smilingface on the user). A modified image or video stream may be presented ina graphical user interface displayed on the client device 102 as soon asthe image or video stream is captured and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured and the selected modification icon remainstoggled. Machine-taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transformation system, may supply the user with additionalinteraction options. Such options may be based on the interface used toinitiate the content capture and selection of a particular computeranimation model (e.g., initiation from a content creator userinterface). In various examples, a modification may be persistent afteran initial selection of a modification icon. The user may toggle themodification on or off by tapping or otherwise selecting the face beingmodified by the transformation system and store it for later viewing orbrowse to other areas of the imaging application. Where multiple facesare modified by the transformation system, the user may toggle themodification on or off globally by tapping or selecting a single facemodified and displayed within a graphical user interface. In someexamples, individual faces, among a group of multiple faces, may beindividually modified, or such modifications may be individually toggledby tapping or selecting the individual face or a series of individualfaces displayed within the graphical user interface.

A story table 314 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 306). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 304 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 312 storesimage data associated with messages for which message data is stored inthe entity table 306. The entity table 306 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 312 and the video table 304.

Trained machine learning technique(s) 307 stores parameters that havebeen trained during training of the surface normal tensor control system224. For example, trained machine learning techniques 307 stores thetrained parameters of one or more neural network machine learningtechniques.

Training images 309 stores a plurality of images that each include atraining portion representing a person depicted in an image and acorresponding ground-truth surface normal tensor. The plurality ofimages stored in the training images 309 includes various depictions ofone or more users wearing different garments together with segmentationsof the garments that indicate which pixels in the images correspond tothe garments and the corresponding surface normal tensors of thetraining portions that depict the bodies. These training images 309 areused by the surface normal tensor control system 224 to train themachine learning technique, as discussed above and below. In some cases,the training images 309 include a plurality of image resolutions ofbodies depicted in the images. The training images 309 can includelabeled and unlabeled image and video data. The training images 309 caninclude a depiction of a whole body of a particular user, an image thatlacks a depiction of any user (e.g., a negative image), a depiction of aplurality of users wearing different garments, and depictions of userswearing garments at different distances from an image capture device.

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server118. The content of a particular message 400 is used to populate themessage table 302 stored within the database 126, accessible by themessaging server 118. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 114. A message 400 is shown to include thefollowing example components:

-   -   message identifier 402: a unique identifier that identifies the        message 400.    -   message text payload 404: text, to be generated by a user via a        user interface of the client device 102, and that is included in        the message 400.    -   message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400. Image data for a sent or received message 400 may        be stored in the image table 312.    -   message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400. Video data        for a sent or received message 400 may be stored in the video        table 304.    -   message audio payload 410: audio data, captured by a microphone        or retrieved from a memory component of the client device 102,        and that is included in the message 400.    -   message augmentation data 412: augmentation data (e.g., filters,        stickers, or other annotations or enhancements) that represents        augmentations to be applied to message image payload 406,        message video payload 408, or message audio payload 410 of the        message 400. Augmentation data for a sent or received message        400 may be stored in the augmentation table 310.    -   message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client 104.    -   message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image within the        message image payload 406, or a specific video in the message        video payload 408).    -   message story identifier 418: identifier values identifying one        or more content collections (e.g., “stories” identified in the        story table 314) with which a particular content item in the        message image payload 406 of the message 400 is associated. For        example, multiple images within the message image payload 406        may each be associated with multiple content collections using        identifier values.    -   message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   message sender identifier 422: an identifier (e.g., a messaging        system identifier, email address, or device identifier)        indicative of a user of the client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 312.Similarly, values within the message video payload 408 may point to datastored within a video table 304, values stored within the messageaugmentation data 412 may point to data stored in an augmentation table310, values stored within the message story identifier 418 may point todata stored in a story table 314, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within an entity table 306.

Surface Normal Tensor Control System

FIG. 5 is a block diagram showing an example surface normal tensorcontrol system 224, according to some examples. Surface normal tensorcontrol system 224 includes a set of components 510 that operate on aset of input data (e.g., a monocular image 501 depicting a real body ofa person and training image data 502). The set of input data is obtainedfrom training images 309 stored in database(s) (FIG. 3 ) during thetraining phases and is obtained from an RGB camera of a client device102 when an AR/VR application is being used, such as by a messagingclient 104. Surface normal tensor control system 224 includes a machinelearning technique module 512, an body segmentation module 514, asurface normal tensor estimation module 517, an AR effect module 519,image modification module 518, and an image display module 520.

During training, the surface normal tensor control system 224 receives agiven training image or video (e.g., monocular image 501 depicting areal body of a person, such as an image of a user wearing as a shirt(short sleeve, t-shirt, or long sleeve), jacket, tank top, sweater, andso forth, a lower body garment, such as pants or a skirt, a whole bodygarment, such as a dress or overcoat, or any suitable combinationthereof or depicting multiple users simultaneously wearing respectivecombinations of upper body garments, lower body garments, or whole bodygarments from training image data 502. The surface normal tensor controlsystem 224 crops out an image portion that includes the real body of theperson. The surface normal tensor control system 224 applies one or moremachine learning techniques using the machine learning technique module512 on the given training image or video portion that has been croppedout.

The machine learning technique module 512 extracts one or more featuresfrom the given training image or video to estimate a segmentation of theperson depicted in the image (e.g., generates a segmentation vector)concurrently with a surface normal tensor of the person. For example,the segmentation of the body identifies which pixels in the imagecorrespond to the body of the user and which pixels correspond to abackground. Namely, the segmentation output by the machine learningtechnique module 512 identifies borders of a body of the user in thegiven training image. The surface normal tensor provides the pixel angledirections or the surface normals of each pixel of the body of theperson depicted in the image.

The machine learning technique module 512 retrieves the ground truthsurface normal tensor associated with the given training image or video.The machine learning technique module 512 compares the estimated surfacenormal tensor of the person with the ground truth garment surface normaltensor provided as part of the training image data 502. Based on adifference threshold or deviation of the comparison, the machinelearning technique module 512 updates one or more coefficients orparameters and obtains one or more additional training images or videos.After a specified number of epochs or batches of training images havebeen processed and/or when the difference threshold or deviation reachesa specified value, the machine learning technique module 512 completestraining and the parameters and coefficients of the machine learningtechnique module 512 are stored in the trained machine learningtechnique(s) 307.

The body segmentation generated by the machine learning technique module512 is provided to the body segmentation module 514. The bodysegmentation module 514 can select or identify a set of pixels in theimage that correspond to the body of the person based on the bodysegmentation received from the machine learning technique module 512.The body segmentation module 514 is used to track a 2D or 3D position ofthe body in subsequent frames of a video. This enables one or more ARelements to be displayed on the body and be maintained at theirrespective positions as the body moves around the screen. In this way,the body segmentation module 514 can determine and track which portionsof the body are currently shown in the image that depicts the person andto selectively adjust the corresponding AR elements that are displayed.

The surface normal tensor estimation module 517 receives a surfacenormal tensor from the machine learning technique module 512. Thesurface normal tensor estimation module 517 can specify the pixeldirections or the surface normals of each pixel in the portion of theimage that is identified by the body segmentation module 514 ascorresponding to the body depicted in the image. In this way, theoutputs of both the body segmentation module 514 and the surface normaltensor estimation module 517 can be used to uniquely and specificallydetermine the surface normals of each pixel that corresponds to the bodydepicted in the image. This enables the AR effect module 519 toselectively apply a given set of AR elements to the body depicted in theimage without modifying any other portion of the image (e.g., thebackground). Also, based on the outputs of both the body segmentationmodule 514 and the surface normal tensor estimation module 517, the AReffect module 519 can apply an AR element to a portion of the body forwhich the surface normals are available for presentation and when thatparticular portion of the body is no longer visible, the AR effectmodule 519 can remove the applied AR element.

After training, surface normal tensor control system 224 receives aninput image 501 (e.g., monocular image depicting a person) as a singleRGB image from a client device 102. The surface normal tensor controlsystem 224 generates a segmentation of the data representing the persondepicted in the image. The surface normal tensor control system 224extracts a portion of the image corresponding to the segmentation of thedata representing the person depicted in the image. The surface normaltensor control system 224 applies a machine learning model to theportion of the image to predict a surface normal tensor for the datarepresenting the depiction of the person, the surface normal tensorrepresenting surface normals of each pixel within the portion of theimage. The surface normal tensor control system 224 applies one or moreAR elements to the image based on the surface normal tensor.

In some examples, the surface normal tensor control system 224determines that light is being focused on the data representing thedepiction of the person from a first direction based on the surfacenormal tensor. The surface normal tensor control system 224 modifiespixel values of the portion of the image corresponding to thesegmentation of the data representing the depiction of the person tore-focus the light on the depiction of the person from a seconddirection, wherein the pixel values are modified without modifying pixelvalues of portions of the image outside of the segmentation. In someexamples, the surface normal tensor control system 224 applies the oneor more AR elements based on a geometry of a body of the person, hair ofthe person, clothing of the person, and one or more accessories worn bythe person. In some examples, the surface normal tensor control system224 applies artificial light to the data representing the persondepicted in the image based on the surface normal tensor.

In some examples, the surface normal tensor control system 224 displaysthe one or more AR elements on a first portion of the data representingthe person depicted in a first frame of a video, wherein the person ispositioned at a first location in the first frame. The surface normaltensor control system 224 determines that the person has moved from thefirst location to a second location in a second frame of the video andupdate a display position of the one or more AR elements in the secondframe to maintain the display of the one or more AR elements on the datarepresenting the person depicted in the image based on the surfacenormal tensor.

In some examples, the surface normal tensor control system 224 replacesdata representing a depiction of the person with one or more visualeffects. In such cases, the surface normal tensor control system 224determines light reflection directions on the person based on thesurface normal tensor and causes the one or more visual effects toreflect light along the light reflection directions using the surfacenormal tensor. For example, the surface normal tensor control system 224recolors one or more portions of the person depicted in the image. Forexample, the surface normal tensor control system 224 applies one ormore animated fashion items to the person depicted in the image based onthe surface normal tensor.

In some examples, the surface normal tensor control system 224determines a first direction of a first pixel corresponding to the datarepresenting a depiction of a person. The surface normal tensor controlsystem 224 determines a second direction of a second pixel correspondingto the data representing a depiction of a person. The surface normaltensor control system 224 generates, for display, a first AR elementthat includes a 3D graphic (e.g., a 3D column or bar) that extends fromthe first pixel along the first direction. The surface normal tensorcontrol system 224 generates, for display together with the first ARelement, a second AR element that includes a 3D graphic (e.g., anotheridentical 3D column or bar) that extends from the second pixel along thesecond direction.

In some examples, the surface normal tensor control system 224 detectsone or more wrinkles of clothing worn by the person depicted in theimage based on the surface normal tensor. In such cases, the surfacenormal tensor control system 224 renders one or more virtual shadows onthe clothing based on the one or more wrinkles. For example, the surfacenormal tensor control system 224 bends a portion of the one or more ARelements that overlays the one or more wrinkles based on the surfacenormal tensor.

The surface normal tensor estimation module 517 is trained to generateor extract values indicating the pixel direction or angle relative to acamera used to capture the image or video depicting the image portioncorresponding to the person or user depicted in the image. The anglerelative to the camera can in some cases be represented as a normalvector or normal direction of a given pixel. For example, the surfacenormal tensor estimation module 517 can determine, for a given pixel,the direction to which the pixel points relative to a surface normal ofa camera that captures the image that includes the pixel. This directioncan be associated with the pixel by storing the direction in a vectorthat includes or defines an x, y, z component in a red, green and blue(RGB) channel of each pixel. This can be referred to as the normal map.Namely, each pixel can be represented by an RGB channel that defines theamount of red, green and blue color to associate with the pixel, such asthe red, green and blue pixel values for each pixel. This RGB channelcan also be associated with a vector that defines the pixel or normaldirection of each pixel in the x, y, z coordinate or UV space. In thisway, each pixel can include red, green and blue values as well as x, yand z coordinates.

For example, if the pixel is on a left shirt sleeve and the personwearing the shirt is turned left relative to the camera, the pixel anglecan be 45 degrees or −45 degrees (or any other suitable angle even anangle that is not facing from the camera) relative to a surface normalof the camera. A larger pixel angle indicates that the portion of theperson represented by the corresponding pixel is turned furtherright/left relative to the camera. The pixel angle can beone-dimensional and/or 2D. In case of being 2D, the pixel anglerepresents how much the person is turned left/right relative to thecamera and also how far up/down the portion of the fashion item ispointing. The surface normal tensor estimation module 517 generates amatrix representing the one-dimensional, 2D pixel angle, and/or 3D pixelangle for each pixel in the portion of the image corresponding to theperson depicted in the image or video. The pixel angle can berepresented by a 3D normal vector or 2D normal vector and stored in theRGB channel of each pixel. In some cases, the surface normal tensorestimation module 517 generates the values independently from movementor tracking information.

A user of the AR/VR application may be presented with an option toselect an AR application or experience to control display of AR elementson the user, such as to re-focus light from a different direction (e.g.,to apply artificial or AR light to the person), to generate columns or3D elements extending from groups of pixels on the person depicted inthe image or video, generate shadows on the person based on wrinklesthat are determined based on a surface normal tensor, recolor one ormore portions of the person, and so forth. In response to receiving auser selection of the option, a camera (e.g., front-facing orrear-facing camera) is activated to begin capturing an image or video ofthe user. The image or video depicting the user is provided to the AReffect module 519 to apply one or more AR elements to the persondepicted in the image or video in accordance with the selected option(e.g., re-focus light from a different direction (e.g., to applyartificial or AR light to the person), to generate columns or 3Delements extending from groups of pixels on the person depicted in theimage or video, generate shadows on the person based on wrinkles thatare determined based on a surface normal tensor, recolor one or moreportions of the person). The AR effect module 519 selects betweenvarious applications/modifications of AR elements displayed on the user,such as based on gestures or movement of the user and applies such ARelements based on a body segmentation and surface normal tensorestimation provided by the machine learning technique module 512.

The image modification module 518 can adjust the image captured by thecamera based on the AR effect selected by the AR effect module 519. Theimage modification module 518 adjusts the way in which the user is/arepresented in an image or video, such as by changing the color orocclusion pattern of the lights and shadows cast on the user based onthe body segmentation and surface normal tensor of the person andapplying one or more AR elements to the person depicted in the image orvideo. Image display module 520 combines the adjustments made by theimage modification module 518 into the received monocular image or videodepicting the user's body. The image or video is provided by the imagedisplay module 520 to the client device 102 and can then be sent toanother user or stored for later access and display.

FIGS. 6-8 show illustrative outputs of one or more of the visual effectsthat can be selected and applied by the AR effect module 519. Forexample, as shown in FIG. 6 , input from a user may be receivedselecting a set of 3D AR graphical elements (e.g., a 3D column or 3Dbar). In response, the surface normal tensor control system 224generates a user interface 600 in which a real-time video 610 ispresented. The real-time video 610 may include a video captured andreceived from a front-facing or rear-facing camera of the client device102. The real-time video 610 may include a depiction of a person 620.

The AR effect module 519 can generate a 3D graphic as the AR elementsthat appears to extend from each group of pixels of person 620 depictedin the video 610. The 3D graphic can be associated with a group ofpixels having a certain quantity of pixels (e.g., 100 pixels). Forexample, a group of pixels that are within a specified region (e.g., asquare region or circular region having a certain size) can beassociated with each respective 3D graphic of multiple identical (ordifferent) 3D graphics. The AR effect module 519 can identify a firstgroup of pixels that are within a first portion of the segmentation ofthe body of the person 620 depicted in the video 610. The AR effectmodule 519 can obtain the surface normal tensor for that first group ofpixels and can generate an average (or some other representative)surface normal that represents the average of the surface normals of thefirst group of pixels.

The AR effect module 519 can then retrieve a first 3D AR graphic 630(e.g., a first AR 3D column) and align the first AR 3D graphic 630 ontop of the first group of pixels and angle the first AR 3D graphic alongthe average of the surface normals of that first group of pixels. Thismakes the first AR 3D graphic 630 appear to be extending from the firstgroup of pixels corresponding to the first portion of the segmentationof the body of the person 620. The AR effect module 519 can perform asimilar operation for a second group of pixels that are within a secondportion of the segmentation to generate a second AR 3D graphic fordisplay on top of the second group of pixels. This process can berepeated until all of the pixel groups within the segmentation of thebody are associated with respective AR 3D graphics. The AR 3D graphicschange their positions and orientations as the person 620 moves aroundin subsequent frames and the surface normals of the respective groups ofpixels are updated.

Other portions of the video 610 (e.g., a background) that fall outsideof the segmentation of the person 620 are not modified or affected bythe displayed AR 3D graphics. In this way, the AR 3D graphics are onlydisplayed on pixels corresponding to the body of the person 620 that isdepicted in the image or video and not on any other portion.

In some examples, as shown in FIG. 7 , input from a user may be receivedselecting an option to adjust a color properties of pixels of a persondepicted in an image or video and/or to adjust shadows or lightreflections and absorptions by the person depicted in the image orvideo. In response, the surface normal tensor control system 224generates a user interface 700 in which a real-time video 710 ispresented. The real-time video 710 may include a video captured andreceived from a front-facing or rear-facing camera of the client device102. The real-time video 710 may include a depiction of a person 712.

The AR effect module 519 can apply a first set of color modifications toa first region 716 that corresponds to or is based on the surfacenormals of the pixels in the first region 716. A second set of colormodifications can be applied to a second region 714 that corresponds toor is based on the surface normals of the pixels in the second region714. Namely, the surface normals of the pixels in the first region 716may be within a first threshold or first set of boundaries. In response,the AR effect module 519 selects and applies the first set of colormodifications to the first region 716. Also, the surface normals of thepixels in the second region 714 may be within a second threshold orsecond set of boundaries that differ from the first threshold or firstset of boundaries. In response, the AR effect module 519 selects andapplies the second set of color modifications to the first region 716.The first set of color modifications can be associated with the firstset of boundaries or first threshold and the second set of colormodifications can be associated with the second set of boundaries ofsecond threshold. Other portions of the video 710 (e.g., a background)that fall outside of the segmentation of the person 712 are not modifiedor affected by the color modifications. In this way, the colormodifications are only displayed on pixels corresponding to the body ofthe person 710 that is depicted in the image or video and not on anyother portion.

In some cases, the person 712 can move around in the video. For example,as shown in FIG. 8 , the person 712 has moved to a new position whichresults in an adjustment of change to the surface normals of the pixelsof the body of the person. Namely, as shown in FIG. 8 , a user interface800 is presented in which a video 810 depicts the person 812 (which canbe the same person 712 as in FIG. 7 but in a new position). Now, the AReffect module 519 can apply a third set of color modifications to thefirst region 816 (corresponding previously to the first region 716) thatcorresponds to or is based on the surface normals of the pixels in thefirst region 816. A fourth set of color modifications can be applied toa second region 814 (corresponding previously to the second region 714)that corresponds to or is based on the surface normals of the pixels inthe second region 714.

In some cases, the person 712 in FIG. 7 remains in the same position buta new color modification is selected. The new color modificationspecifies different sets of color modifications to be applied to pixelsin different boundaries (ranges) or thresholds. In such cases, as shownin FIG. 8 , a user interface 800 is presented in which a video 810depicts the person 812 (which can be the same person 712 as in FIG. 7 ).Now, the AR effect module 519 can apply a third set of colormodifications to the first region 816 (corresponding previously to thefirst region 716) that corresponds to or is based on the surface normalsof the pixels in the first region 816. A fourth set of colormodifications can be applied to a second region 814 (correspondingpreviously to the second region 714) that corresponds to or is based onthe surface normals of the pixels in the second region 714. For example,input from the user can be received to reduce a size of the boundariesfor specific sets of color modifications relative to those shown in FIG.7 . In such cases, the same sets of pixels that had surface normals thatwere within the thresholds or boundaries may no longer be within thethresholds or boundaries. As a result, the pixels can remain unmodifiedor different sets of colors can be applied to such pixels.

In some examples, the surface normal tensor control system 224 generatesa user interface 800 in which a real-time video 720 is presented. Thereal-time video 720 may include a video captured and received from afront-facing or rear-facing camera of the client device 102. Thereal-time video 720 may include a depiction of a person 722.

The AR effect module 519 can apply a first brightness or generate afirst set of shadows in a region 724 that corresponds to or is based onthe surface normals of the pixels in the region 724. Namely, light(artificial or real) can be projected on the person 722 from aparticular angle. The shadows can be cast or rendered artificially onthe person 722 (e.g., on the clothing of the person) based on thesurface normals of the pixels in the region 724. In some cases, awrinkle may be present on the clothing of the person 724. The wrinklecan result in a set of surface normals that indicate the direction alongwhich light is absorbed or reflected and can be used to render theshadows on the person 722 or the clothing of the person 722 depicted inthe video 720. As the person moves around in the video, the light canremain cast from the same but the shadows that are generated can beadjusted as the surface normals of the pixels in that region change.

In some cases, input can be received from a user that (adjusts)increases the amount of (artificial or real) light or decreases theamount of light cast on the person 722. Based on the adjustment to theamount of light, the shadows cast on the person can be increased ordecreased. For example, as shown in FIG. 8 , a user interface 800 ispresented in which a video 820 depicts the person 822 (which can be thesame person 722 as in FIG. 7 ). Now, the AR effect module 519 can applya different amount of shadows to the region 824 (correspondingpreviously to the region 724) reflecting the adjustments to the amountof light.

In some examples, an AR element can be used to completely replace thedepiction of the person in the image or a portion of the person (e.g.,one or more articles of clothing). The AR element can be animated andcan be a 2D element or 3D element. The AR element can be overlaid on topof clothing worn by the user. A portion of the AR element can be twistedor bent in a region of the clothing that has a crease or wrinkle. Thismakes it appear as though the AR element is actually part of theclothing worn by the person depicted in the image. Specifically, the ARelement can be overlaid and bent or twisted in a way that results in thesurface normals of the AR element mirroring, copying or corresponding tothe respective surface normals of the pixels over which the AR elementis overlaid.

FIG. 9 is a flowchart of a process 900 performed by the surface normaltensor control system 224, in accordance with some examples. Althoughthe flowchart can describe the operations as a sequential process, manyof the operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed. A process may correspondto a method, a procedure, and the like. The steps of methods may beperformed in whole or in part, may be performed in conjunction with someor all of the steps in other methods, and may be performed by any numberof different systems or any portion thereof, such as a processorincluded in any of the systems.

At operation 901, the surface normal tensor control system 224 (e.g., aclient device 102 or a server) receives an image that includes adepiction of a person, as discussed above.

At operation 902, the surface normal tensor control system 224 generatesa segmentation of the data representing the person depicted in theimage, as discussed above.

At operation 903, the surface normal tensor control system 224 extractsa portion of the image corresponding to the segmentation of the datarepresenting the person depicted in the image, as discussed above.

At operation 904, the surface normal tensor control system 224 applies amachine learning model to the portion of the image to predict a surfacenormal tensor for the data representing the depiction of the person, thesurface normal tensor representing surface normals of each pixel withinthe portion of the image, as discussed above.

At operation 905, the surface normal tensor control system 224 appliesone or more augmented reality (AR) elements to the image based on thesurface normal tensor e, as discussed above.

Machine Architecture

FIG. 10 is a diagrammatic representation of the machine 1000 withinwhich instructions 1008 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1000to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1008 may cause the machine 1000to execute any one or more of the methods described herein. Theinstructions 1008 transform the general, non-programmed machine 1000into a particular machine 1000 programmed to carry out the described andillustrated functions in the manner described. The machine 1000 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1000 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1000 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 1008, sequentially or otherwise,that specify actions to be taken by the machine 1000. Further, whileonly a single machine 1000 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 1008 to perform any one or more of themethodologies discussed herein. The machine 1000, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 1000 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 1000 may include processors 1002, memory 1004, andinput/output (I/O) components 1038, which may be configured tocommunicate with each other via a bus 1040. In an example, theprocessors 1002 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 1006 and a processor 1010 that execute the instructions 1008.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 10 shows multiple processors 1002, the machine 1000 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 1004 includes a main memory 1012, a static memory 1014, and astorage unit 1016, all accessible to the processors 1002 via the bus1040. The main memory 1004, the static memory 1014, and the storage unit1016 store the instructions 1008 embodying any one or more of themethodologies or functions described herein. The instructions 1008 mayalso reside, completely or partially, within the main memory 1012,within the static memory 1014, within machine-readable medium 1018within the storage unit 1016, within at least one of the processors 1002(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1000.

The I/O components 1038 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1038 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1038 mayinclude many other components that are not shown in FIG. 10 . In variousexamples, the I/O components 1038 may include user output components1024 and user input components 1026. The user output components 1024 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 1026 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 1038 may include biometriccomponents 1028, motion components 1030, environmental components 1032,or position components 1034, among a wide array of other components. Forexample, the biometric components 1028 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 1030 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, and rotation sensorcomponents (e.g., gyroscope).

The environmental components 1032 include, for example, one or morecameras (with still image/photograph and video capabilities),illumination sensor components (e.g., photometer), temperature sensorcomponents (e.g., one or more thermometers that detect ambienttemperature), humidity sensor components, pressure sensor components(e.g., barometer), acoustic sensor components (e.g., one or moremicrophones that detect background noise), proximity sensor components(e.g., infrared sensors that detect nearby objects), gas sensors (e.g.,gas detection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad, or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera, and a depth sensor, for example.

The position components 1034 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1038 further include communication components 1036operable to couple the machine 1000 to a network 1020 or devices 1022via respective coupling or connections. For example, the communicationcomponents 1036 may include a network interface component or anothersuitable device to interface with the network 1020. In further examples,the communication components 1036 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), WiFi® components, and othercommunication components to provide communication via other modalities.The devices 1022 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1036 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1036 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1036, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 1012, static memory 1014, andmemory of the processors 1002) and storage unit 1016 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 1008), when executedby processors 1002, cause various operations to implement the disclosedexamples.

The instructions 1008 may be transmitted or received over the network1020, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1036) and using any one of several well-known transfer protocols (e.g.,HTTP). Similarly, the instructions 1008 may be transmitted or receivedusing a transmission medium via a coupling (e.g., a peer-to-peercoupling) to the devices 1022.

Software Architecture

FIG. 11 is a block diagram 1100 illustrating a software architecture1104, which can be installed on any one or more of the devices describedherein. The software architecture 1104 is supported by hardware such asa machine 1102 that includes processors 1120, memory 1126, and I/Ocomponents 1138. In this example, the software architecture 1104 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1104 includes layerssuch as an operating system 1112, libraries 1110, frameworks 1108, andapplications 1106. Operationally, the applications 1106 invoke API calls1150 through the software stack and receive messages 1152 in response tothe API calls 1150.

The operating system 1112 manages hardware resources and provides commonservices. The operating system 1112 includes, for example, a kernel1114, services 1116, and drivers 1122. The kernel 1114 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1114 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionalities. The services 1116 canprovide other common services for the other software layers. The drivers1122 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1122 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 1110 provide a common low-level infrastructure used byapplications 1106. The libraries 1110 can include system libraries 1118(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1110 can include APIlibraries 1124 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in 2D and 3D in a graphiccontent on a display), database libraries (e.g., SQLite to providevarious relational database functions), web libraries (e.g., WebKit toprovide web browsing functionality), and the like. The libraries 1110can also include a wide variety of other libraries 1128 to provide manyother APIs to the applications 1106.

The frameworks 1108 provide a common high-level infrastructure that isused by the applications 1106. For example, the frameworks 1108 providevarious graphical user interface functions, high-level resourcemanagement, and high-level location services. The frameworks 1108 canprovide a broad spectrum of other APIs that can be used by theapplications 1106, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1106 may include a home application1136, a contacts application 1130, a browser application 1132, a bookreader application 1134, a location application 1142, a mediaapplication 1144, a messaging application 1146, a game application 1148,and a broad assortment of other applications such as an externalapplication 1140. The applications 1106 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1106, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the external application1140 (e.g., an application developed using the ANDROID™ or IOS™ SDK byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the external application 1140 can invoke the API calls 1150provided by the operating system 1112 to facilitate functionalitydescribed herein.

Glossary

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistant (PDA), smartphone, tablet, ultrabook, netbook, laptop,multi-processor system, microprocessor-based or programmable consumerelectronics, game console, set-top box, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1xRTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions.

Components may constitute either software components (e.g., codeembodied on a machine-readable medium) or hardware components. A“hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various examples, one or more computer systems (e.g., astandalone computer system, a client computer system, or a servercomputer system) or one or more hardware components of a computer system(e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an ASIC. A hardware componentmay also include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. For example, ahardware component may include software executed by a general-purposeprocessor or other programmable processor. Once configured by suchsoftware, hardware components become specific machines (or specificcomponents of a machine) uniquely tailored to perform the configuredfunctions and are no longer general-purpose processors. It will beappreciated that the decision to implement a hardware componentmechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software), may bedriven by cost and time considerations. Accordingly, the phrase“hardware component”(or “hardware-implemented component”) should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein.

Considering examples in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inexamples in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1002 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some examples, the processors or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo, and the like. The access time for the ephemeral message may beset by the message sender. Alternatively, the access time may be adefault setting or a setting specified by the recipient. Regardless ofthe setting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines, anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,” and“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

Changes and modifications may be made to the disclosed examples withoutdeparting from the scope of the present disclosure. These and otherchanges or modifications are intended to be included within the scope ofthe present disclosure, as expressed in the following claims.

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors of a client device, an image that includes data representinga depiction of a person; generating, by the one or more processors, asegmentation of the data representing the depiction of the person;extracting a portion of the image corresponding to the segmentation ofthe data representing the person depicted in the image; applying amachine learning model to the portion of the image to predict a surfacenormal tensor for the data representing the depiction of the person, thesurface normal tensor representing surface normals of each pixel withinthe portion of the image; and applying one or more augmented reality(AR) elements to the image based on the surface normal tensor.
 2. Themethod of claim 1, wherein applying the one or more AR elementscomprises: determining that light is being focused on the datarepresenting the depiction of the person from a first direction based onthe surface normal tensor; and modifying pixel values of the portion ofthe image corresponding to the segmentation of the data representing thedepiction of the person to re-focus the light on the depiction of theperson from a second direction, wherein the pixel values are modifiedwithout modifying pixel values of portions of the image outside of thesegmentation.
 3. The method of claim 1, wherein applying the one or moreAR elements comprises applying the one or more AR elements based on ageometry of a body of the person, hair of the person, clothing of theperson, and one or more accessories worn by the person.
 4. The method ofclaim 1, wherein applying the one or more AR elements comprises applyingartificial light to the data representing the person depicted in theimage based on the surface normal tensor.
 5. The method of claim 1,further comprising: displaying the one or more AR elements on a firstportion of the data representing the person depicted in a first frame ofa video, wherein the person is positioned at a first location in thefirst frame; determining that the person has moved from the firstlocation to a second location in a second frame of the video; andupdating a display position of the one or more AR elements in the secondframe to maintain the display of the one or more AR elements on the datarepresenting the person depicted in the image based on the surfacenormal tensor.
 6. The method of claim 1, wherein the surface normaltensor is computed relative to a surface normal of a camera used tocapture the image.
 7. The method of claim 1, wherein the AR elements areapplied to a real-time video feed comprising the image.
 8. The method ofclaim 1, wherein applying the one or more AR elements comprisesreplacing data representing a depiction of the person with one or morevisual effects, further comprising: determining light reflectiondirections on the person based on the surface normal tensor; and causingthe one or more visual effects to reflect light along the lightreflection directions using the surface normal tensor.
 9. The method ofclaim 8, wherein applying the one or more AR elements comprisesrecoloring one or more portions of the person depicted in the image. 10.The method of claim 1, wherein applying the one or more AR elementscomprises applying one or more animated fashion items to the persondepicted in the image based on the surface normal tensor.
 11. The methodof claim 1, wherein applying the one or more AR elements comprises:determining a first direction of a first pixel corresponding to the datarepresenting a depiction of a person; determining a second direction ofa second pixel corresponding to the data representing a depiction of aperson; generating, for display, a first AR element comprising athree-dimensional (3D) graphic that extends from the first pixel alongthe first direction; and generating, for display together with the firstAR element, a second AR element comprising a 3D graphic that extendsfrom the second pixel along the second direction.
 12. The method ofclaim 11, wherein the 3D graphic that extends from the first pixelcomprises a 3D column.
 13. The method of claim 1, wherein the machinelearning model comprises a neural network, the neural network beingtrained to establish a relationship between image portions depictingdifferent orientations of human bodies and surface normal directions ofpixels of the human bodies.
 14. The method of claim 13, furthercomprising training the machine learning model by performing operationscomprising: receiving a plurality of training data sets, each of theplurality of training data sets comprising a training portionrepresenting a person depicted in an image and a correspondingground-truth surface normal tensor; applying the machine learning modelto a first training portion of a first training data set to predict anestimated surface normal tensor; computing a deviation between theestimated surface normal tensor and the ground-truth surface normaltensor associated with the first training portion; updating one or moreparameters of the machine learning model based on the computeddeviation.
 15. The method of claim 1, further comprising: detecting oneor more wrinkles of clothing worn by the person depicted in the imagebased on the surface normal tensor.
 16. The method of claim 15, whereinapplying the one or more AR elements comprises rendering one or morevirtual shadows on the clothing based on the one or more wrinkles. 17.The method of claim 15, wherein applying the one or more AR elementscomprises bending a portion of the one or more AR elements that overlaysthe one or more wrinkles based on the surface normal tensor.
 18. Themethod of claim 1, wherein the machine learning model generates asegmentation vector that associates each pixel in the image with anindication of whether the pixel corresponds to a background or the datarepresenting the depiction of the person, the one or more AR elementsbeing applied further based on the segmentation vector.
 19. A systemcomprising: a processor of a client device; and a memory componenthaving instructions stored thereon that, when executed by the processor,cause the processor to perform operations comprising: receiving an imagethat includes data representing a depiction of a person; generating asegmentation of the data representing the person depicted in the image;extracting a portion of the image corresponding to the segmentation ofthe data representing the person depicted in the image; applying amachine learning model to the portion of the image to predict a surfacenormal tensor for the data representing the depiction of the person, thesurface normal tensor representing surface normals of each pixel withinthe portion of the image; and applying one or more augmented reality(AR) elements to the image based on the surface normal tensor.
 20. Anon-transitory computer-readable storage medium having stored thereoninstructions that, when executed by a processor of a client device,cause the processor to perform operations comprising: receiving an imagethat includes data representing a depiction of a person; generating asegmentation of the data representing the person depicted in the image;extracting a portion of the image corresponding to the segmentation ofthe data representing the person depicted in the image; applying amachine learning model to the portion of the image to predict a surfacenormal tensor for the data representing the depiction of the person, thesurface normal tensor representing surface normals of each pixel withinthe portion of the image; and applying one or more augmented reality(AR) elements to the image based on the surface normal tensor.